根据值对熊猫数据框中的行或列进行排序
在本文中，让我们讨论如何根据值对熊猫数据框中的行或列进行排序。Pandas sort_values() 方法按照传递列的升序或降序对数据帧进行排序。它不同于排序的 Python 函数，因为它不能对数据框进行排序，也不能选择特定的列。
语法: DataFrame.sort_values(by，axis=0，升序=True，inplace=False，kind='quicksort '，na_position='last ')
参数:此方法将采用以下参数: by: 单个/列名列表对数据框进行排序。 轴:行为 0 或“索引”，列为 1 或“列”。 升序:布尔值，如果为真，则按升序对数据框进行排序。 在位:布尔值。如果为真，则对传递的数据框本身进行更改。 种类:可以有三个输入(“quicksort”、“mergesort”或“heapsort”)的用于对数据帧进行排序的算法的字符串。 na_position: 取两个字符串输入‘last’或‘first’来设置 Null 值的位置。默认值为“最后”。
返回类型:返回与函数调用方数据框具有相同维度的排序数据框。
现在，让我们创建一个示例数据帧:
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# show the dataframe
details
输出:
示例 1: 基于单个列对数据框行进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# Sort the rows of dataframe by 'Name' column
rslt_df = details.sort_values(by = 'Name')
# show the resultant Dataframe
rslt_df
输出:
示例 2: 基于多列对数据框行进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ananya', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Priya', 35, 'Mp', 'Geu'),
('Priya', 34, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# sort Dataframe rows based on a 'Name' & 'Age' columns
# if duplicate value is present in 'Name' column
# then sorting will be done according to 'Age' column
rslt_df = details.sort_values(by = ['Name', 'Age'])
# show the resultant Dataframe
rslt_df
输出:
示例 3: 按降序对基于列的数据框行进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ananya', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Priya', 35, 'Mp', 'Geu'),
('Priya', 34, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# sort Dataframe rows based on "Name'
# column in Descending Order
rslt_df = details.sort_values(by = 'Name', ascending = False)
# show the resultant Dataframe
rslt_df
输出:
示例 4: 基于原地列对数据框行进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ananya', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Priya', 35, 'Mp', 'Geu'),
('Priya', 34, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =[ 'b', 'c', 'a', 'e', 'f',
'g', 'i', 'j', 'k', 'd'])
# Sort the rows of dataframe by 'Name'
# column inplace
details.sort_values(by = 'Name', inplace = True)
# show the resultant Dataframe
details
输出:
让我们看看另一个简单的数据框架，在这个框架上，我们可以根据行对列进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [
(75, 50, 60, 70),
(75, 55, 65, 75),
(75, 35, 45, 25),
(75, 90, 60, 70),
(76, 90, 70, 60),
(90, 80, 70, 60),
(65, 10, 30, 20)
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Hindi', 'Math',
'Science', 'English'],
index = ['Ankit', 'Rahul', 'Aishwarya',
'Shivangi', 'Priya', 'Swapnil',
'Shaurya'])
# show the dataframe
details
输出:
示例 1: 基于单行对数据框的列进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [
(75, 50, 60, 70),
(75, 55, 65, 75),
(75, 35, 45, 25),
(75, 90, 60, 70),
(76, 90, 70, 60),
(90, 80, 70, 60),
(65, 10, 30, 20)
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Hindi', 'Math',
'Science', 'English'],
index = ['Ankit', 'Rahul', 'Aishwarya',
'Shivangi', 'Priya', 'Swapnil',
'Shaurya'])
# sort columns of a Dataframe based
# on a 'Shivangi' row
rslt_df = details.sort_values(by = 'Shivangi', axis = 1)
# show the dataframe
rslt_df
输出:
示例 2: 根据单行按降序对数据框的列进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [
(75, 50, 60, 70),
(75, 55, 65, 75),
(75, 35, 45, 25),
(75, 90, 60, 70),
(76, 90, 70, 60),
(90, 80, 70, 60),
(65, 10, 30, 20)
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Hindi', 'Math',
'Science', 'English'],
index = ['Ankit', 'Rahul', 'Aishwarya',
'Shivangi', 'Priya', 'Swapnil',
'Shaurya'])
# Sort columns of a dataframe in descending order
# based on a 'Shivangi' row
rslt_df = details.sort_values(by = 'Shivangi', axis = 1, ascending = False)
rslt_df
输出:
示例 3: 基于多行对数据框的列进行排序。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [
(75, 50, 60, 70),
(75, 55, 65, 75),
(75, 35, 45, 25),
(75, 90, 60, 70),
(76, 90, 70, 60),
(90, 80, 70, 60),
(65, 10, 30, 20)
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Hindi', 'Math',
'Science', 'English'],
index = ['Ankit', 'Rahul', 'Aishwarya',
'Shivangi', 'Priya', 'Swapnil',
'Shaurya'])
# sort Dataframe columns based on a 'Shivangi' & 'Priya' rows
# if duplicate value is present in 'Shivangi' row
# then sorting will be done according to 'Priya' row
rslt_df = details.sort_values(by = ['Shivangi', 'Priya'], axis = 1)
rslt_df
输出:
